Learn Data Science – Do Programming using Python & R

Pre-requisite

Good news for enrolled candidates of Data Science training where they will get chance to attend FREE sessions on Mathematics which are pre-requisite required to accomplish Data Science training, see syllabus and other details here

Key Features

• No PPT’s completely Hands-on Data Science – R programming training.
• Installation required in your laptop for training
• For MAC system download link of Python, NumPy, SciPy & matplotlib get from here
• All at only 14000 INR

Why to choose Data Science as career?

 Python basics  1) Introduction 2) Data types and operator 3) List tuples and dictionaries 4) Object oriented 5) Exceptions handling 6) File handling 7) Modules NumPy  Introduction Environment Ndarray Object Data Types Array Attributes Array Creation Routines Array from Existing Data Array From Numerical Ranges Indexing Slicing Broadcasting Array Manipulation Binary Operators String Functions Mathematical Functions Arithmetic Operations Statistical Functions Sort, Search & Counting Functions Byte Swapping Copies & Views Matrix Library Linear Algebra I/O with NumPy Python Pandas  Introduction Data Structures Series DataFrame Panel Basic Functionality Descriptive Statistics Function Application Reindexing Iteration Sorting Text Data Options Customization Indexing Selecting Data Statistical Functions Window Functions Aggregations Missing Data GroupBy Merging/Joining Concatenation Date Functionality Timedelta Categorical Data Visualization IO Tools Sparse Data Data Loading, Storage, and File Formats  Reading and Writing Data in Text Format Reading Text Files in Pieces Writing Data Out to Text Format Manually Working with Delimited Formats JSON Data XML and HTML: Web Scraping matplotlib API  Figures and Subplots Colors, Markers, and Line Styles Ticks, Labels, and Legends Subplot Saving Plots to File matplotlib Configuration Plotting Functions in pandas Line Plots Bar Plots Histograms and Density Plots Scatter Plots Python Visualization Tool Ecosystem ETL operations SciPy  Introduction Basic Functionality Cluster Constants FFTpack Integrate Interpolate Input and Output Linalg Ndimage Optimize Stats CSGraph Spatial ODR

 Introduction to R  Introduction to R R Packages R Programming R Programming if statements for statements while statements repeat statements break and next statements switch statement scan statement Executing the commands in a File Data structures Vector Matrix Array Data frame List Functions DPLYR & apply Function Import Data File DPLYP – Selection DPLYP – Filter DPLYP – Arrange DPLYP – Mutate DPLYP – Summarize Data visualization in R Bar chart, Dot plot Scatter plot, Pie chart Histogram and Box plot Heat Maps World Cloud Introduction to statistics  Type of Data Distance Measures (Similarity, dissimilarity, correlation) Euclidean space. Manhattan Minkowski Cosine similarity Mahalanobis distance Pearson’s correlation coefficient Probability Distributions Hypothesis Testing I Hypothesis Testing Introduction Hypothesis Testing – T Test, Anova Hypothesis Testing II Hypothesis Testing about population Chi Square Test F distribution and F ratio Regression Analysis Regression Linear Regression Models Non Linear Regression Models Classification  Classification Decision Tree Logistic Regression Bayesian Support Vector Machinesa Clustering K-means Clustering and Case Study DBSCAN Clustering and Case study Hierarchical Clustering Association Apriori Algorithm Candidate Generation Visualization on Associated Rules